EP2631851A1 - Modèle numérique de données de consommation et enregistrement analytique du client - Google Patents

Modèle numérique de données de consommation et enregistrement analytique du client Download PDF

Info

Publication number
EP2631851A1
EP2631851A1 EP12425042.4A EP12425042A EP2631851A1 EP 2631851 A1 EP2631851 A1 EP 2631851A1 EP 12425042 A EP12425042 A EP 12425042A EP 2631851 A1 EP2631851 A1 EP 2631851A1
Authority
EP
European Patent Office
Prior art keywords
customer
data
entity
digital
consumption
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
EP12425042.4A
Other languages
German (de)
English (en)
Inventor
Astrid Bohe
Gianluca Cervini
Gianluca Zobi
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Accenture Global Services Ltd
Original Assignee
Accenture Global Services Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Accenture Global Services Ltd filed Critical Accenture Global Services Ltd
Priority to EP12425042.4A priority Critical patent/EP2631851A1/fr
Priority to CA2808096A priority patent/CA2808096C/fr
Priority to US13/779,115 priority patent/US9536002B2/en
Priority to CN201310061925.2A priority patent/CN103295148B/zh
Priority to CA2955707A priority patent/CA2955707C/fr
Publication of EP2631851A1 publication Critical patent/EP2631851A1/fr
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/957—Browsing optimisation, e.g. caching or content distillation
    • G06F16/9574—Browsing optimisation, e.g. caching or content distillation of access to content, e.g. by caching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/40—Network security protocols
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/238—Interfacing the downstream path of the transmission network, e.g. adapting the transmission rate of a video stream to network bandwidth; Processing of multiplex streams

Definitions

  • Telecommunications companies now offer a wide variety of services to their customers, including providing digital content.
  • Digital content includes applications, downloading and streaming media, and online purchases.
  • media companies offering digital content are now expanding to include telecommunications services. These companies, which have traditionally offered one of these products but now have expanded to include both, can be thought of as convergent companies.
  • the solutions described herein include a data model and a customer analytic record (CAR), along with a procedure to generate the CAR.
  • CAR customer analytic record
  • a data model includes information of different business lines at convergent customer level, including digital consumer entities, metrics, and dimensions.
  • the data model is a set of entities related each other through an entity-relationship (E-R) diagram.
  • the entities can be related to, for example: customer socio-demographics, external market researches, interactions between customer and company, traffic, revenues, profit, products and services subscriptions, digital products purchases and consuming, and tariff plans at the customer level.
  • the data model is the base for the analytic data mart and analytic dashboards to be presented to and used by the company in a number of ways including alerts, structure queries, feeds for ad hoc reporting, dashboards for static reporting, and insight case management. Any of these can be formatted and digested to be presented to marketing and business managers within the company in any appropriate format.
  • CAR customer analytic record
  • the data associated with a given customer comes from multiple separate data storage locations in order to collect customer activity data associated with different product lines, including consumption of digital content.
  • the extracted data is processed and combined into a data structure to form a customer analytic record (CAR) that more completely and accurately describes the full range of the customer's behavior with respect to all offerings of the business.
  • CAR customer analytic record
  • the data model includes customer demographic information, and even known customer attitudes expressed through surveys and online. Once generated, the CAR can support a variety of analytical processes. Multiple dimensions of the customer profile, including patterns of digital consumption, can be used to identify groups of customers with similar traits.
  • Predictions can be made about each consumer segment regarding customers' profitability, the likelihood of chum, and what upsell and retention efforts are most likely to receive a positive response. This allows product offerings, incentive programs, and sales events to be targeted to those segments of the customer base where they can be most profitable, which can potentially decrease marketing costs while increasing results relative to traditional, broad-based promotions. Upsell efforts based on identifying commonalities in digital consumption behaviors can also lead to additional revenue based particularly on the customer's digital content activities relative to other similar customers. The outputs of this modeling can then enrich the data model.
  • the data model is a set of structured data. Much of the information is at the customer level.
  • the data model is a set of entities related to each other through an entity-relationship (E-R) diagram.
  • E-R entity-relationship
  • E-R diagram 100 representing one data model that may be appropriate for a convergent company including digital consumer data, is represented in FIG. 1 .
  • E-R diagram 100 represents a consumer-centric data model, in which the consumer, represented in the center of the diagram 100 as entity 102, forms relationships with entities associated with a variety of different businesses and systems as shown.
  • Entity data may come from a web portal 104, from external research channels 106, from a rating, billing, and invoicing system 108, from customer relationship management systems 110, from data warehouses and other systems.
  • Each of the entities and its relationship to the customer 102 may be represented by a variety of different metrics and dimensions.
  • entity data may include:
  • Metrics and dimensions are defined individually for each entity.
  • an interactions entity may include the number of interactions (metric) and reason of interaction (dimension).
  • a digital consumption entity may include quantity and volume (metrics) along with content rating, age rating, distributing platform, connection speed class, payment method, price class, timeband, time, category, subcategory (dimensions).
  • metric the number of interactions
  • dimension dimension
  • metrics quantity and volume
  • Digital consumption data may include data generated from a variety of activities characteristic of a digital consumer. For example, the customer may choose to download applications representing a variety of categories. Digital consumption data may identify information on the time of purchase, the cost of the application, and the category the application falls into along with any appropriate subcategory. Each application may be classified into one or more categories and subcategories, which may depend not only on the nature of the application but also elements important to the company's continuing relationship with the customer. Ideally, the categories within which a customer has purchased applications will allow a company to target the customer for further application purchases as well as other available services.
  • Application categories may include, for example: tools and utilities, business, shopping, travel, sports, social, news, and games.
  • Tool and utility applications assist the user with desktop management, messing/chat, playing and editing multimedia, word processing, spreadsheets, and drivers.
  • Subcategories of tools applications may include translator, calculator, and education applications.
  • Business applications provide functionality for business interoperability with mail and calendar, administrative control data, synchronization, message security, and resource planning.
  • Business application subcategories may include finance and management.
  • Shopping applications allow customers to purchase products with mobile devices, perform price comparison and research, and locate shops selling specific products.
  • Shopping application subcategories may include entertainment, electronics, and food.
  • Travel applications include Cartography and GPS, as well as the reservation and purchase of cruises, last-minutes, playground, flights, car rental, travel agencies, hotels, and tours. Travel subcategories may include last-minutes, hotels, rental cars, and GPS. Sports applications may focus on specific sports, diet, monitoring performance, training, food, and sporting events. Sports subcategories may include real-time news, match forecast, and match streaming. Social applications allow the customer to connect and manage social networks, share and compare affinity tests, make friends, attend social events, or even find a mate. Subcategories for social applications may include chat and sharing. News applications include weather, newspaper, TV news, and radio, each of which may have its own subcategory. Game applications may include all sorts of games available for play on the mobile device. Game application subcategories may exist for each genre of game, such as sports, virtual life, and strategy.
  • Digital consumption data may further include e-commerce, defined as shopping for conventional retail products over the internet rather than at a retail store.
  • E-commerce categories include books (books, magazines, newspapers, e-books, audiobooks), apparel (clothing, shoes, handbags, accessories, luggage, watches, jewelry), computers and office (laptops, netbooks, tablets, printers and ink, devices and accessories, servers and desktops, software, gaming consoles, media players, internet TV), electronics (TV & video, Hi-fi & home theatre, Cameras, cell phones & accessories, video games & mp3 players, Car & GPS, home appliances, musical instruments, general accessories), health and beauty (natural and bio food, health products, personal care and beauty), entertainment (DVD and Blu-Ray, video games, music CDs), home and garden (Kitchen & Dining, Bedding & Bath, Furniture & Décor, Outdoor living, Lawn & Garden
  • Digital consumption data may further include streaming and downloading of digital content.
  • categories and subcategories may provide valuable consumer information.
  • Categories of digital content streaming and downloading may include music (further categorized by musical genre, such as classical, dance, rock, pop, romantic, electronic, country, R&B), images (further categorized by image quality, as well as by subcategories such as Animals and Nature, Cartoons/Comics, Celebrities, Food and Beverages, Holidays and Events, Sport and Outdoors, Office, kids, Landscapes).
  • Digital consuming metrics may be aggregated by time and dimension values.
  • Data dimensions may include content rating, age rating, distributing platform, connection speed class, payment method, device used for consuming, price class, and timeband.
  • the digital consumption data may be aggregated for a specific period of time and may be returned in a number of ways according to the needs of any specific analysis tool.
  • the data to feed the Data model comes from various sources, internal and external. Internal sources are related to Company systems, while external sources are related to the Internet (web sites other than the company portal) or External Market Researches. Both internal and external raw data are converted according to predefined formats and templates, also known as "data interface agreements", in order to be mapped to the data model in a standard way.
  • Telecommunications data may include incoming and outgoing activates, including placed calls and texts. Without violating the customer's privacy, it is possible for the analytic record to indicate how much total time is associated with the customer's most-called numbers, how many different numbers the customer has called or texted, and how many total calls or texts the customer has received rather than how many calls or texts were received from the most connected number. These indicators are useful to measure the networking habits of the customer.
  • this telecommunications data may provide the company with opportunities to further a positive business relationship with the customer by offering the customer opportunities that reflect the customer's usage.
  • Demographics data including all of the demographics factors generally used when managing telecommunications data as well as those particularly relevant for digital consumption, may also be included in the data model.
  • FIG. 2 illustrates an exemplary approach for generating a customer analytic record (CAR).
  • Data stores 200 reflect customer data that may be stored in different locations using diverse systems and nonstandard formatting. Examples of data stores 200 include customer records associated with telecommunications accounts and purchases, represented by data store 200a, customer records associated with digital content downloads and purchases, represented by data store 200b, and demographic data for customers, represented by data store 200c. Other data from other sources may be included.
  • a procedure is designed on how to feed the CAR according to predefined and configurable analytical transformation functions.
  • the data is previously aggregated in a customer-centered fashion; that is, data associated with a single customer across multiple data stores 200 or multiple periods of time (more granular than required data aggregation) or multiple events (transactions) is aggregated together. Since the stores 200 from which the data is retrieved may not all store the data in the same format, the procedure is designed to sanitize and standardize the data in order to fit the customer-centered data structures into which the data is aggregated.
  • the customer analytic records 208 are produced.
  • the CAR 208 is a list of records where each record refers to a unique customer and each field is a variable related to the customer.
  • Each customer analytic record represents all of the collected and aggregated data (base variables) as well as further analytical transformations to derive powerful indicators (calculated variables) associated with one customer.
  • the data structures representing each CAR 208 may involve many hundreds of variables representing customer data stored in a variety of ways.
  • the CAR 208 may include both base variables 210 and calculated variables 212.
  • the base variables 210 are those made available by the data stores 200 along with any other data acquired and aggregated in order to include in the CAR 208. If the CAR 208 is associated with a dedicated analytical data mart 204, as it often will be, the variables 210 are often metrics and dimensions associated with entities of a data model associated with the data mart 204.
  • calculated variables 212 are calculated upon variables extracted from the data stores and historicized in the analytical data mart 204 according to predefined transformation rules.
  • Calculated variables 212 may be stored in accordance with transformation functions 206 which are included as part of the CAR architecture. These operations may reorganize and tabulate existing data to produce values of interest for further analysis. Examples of functions and rules to determine additional variables may include:
  • Some or all of these operations may be easily configured to be performed during the initial generation or later update of a customer analytic record.
  • the operations may be performed on existing data (including any of the existing base and/or calculated variables) on the request of any analytic process or record system.
  • the data may come from a variety of different sources.
  • Third party data providers may tabulate consumer, competitor, and marketplace data.
  • the CAR may include data from a variety of internal and external systems, as earlier explained. Further data can be related to external sources such as social network, internet, and geospatial data.
  • source data is provided by the company according to predefined formats or templates. If this analysis is conducted by an outside party, the data may be predefined according to an "interface agreement" or other explicitly defined arrangement.
  • FIG. 3 is a flowchart illustrating a process 300 by which a customer analytic record may be created and used according to some implementations.
  • Data is submitted or generated for use in the source system (302).
  • the data may come from a variety of different sources.
  • Third party data providers may tabulate consumer, competitor, and marketplace data.
  • the system may include or may extract data from a variety of internal and external systems, as earlier explained; this data may include past and current information about customers, suppliers, products, and the company's business situation (revenue, profits, etc). Further data can be scraped from external sources such as social network, internet, and geospatial data.
  • This raw data may then be extracted, transformed, and loaded into more usable forms (304).
  • This process may be dictated by the method of acquiring the data and the nature of the data itself; for example, unstructured data scraped from the web may need to be filtered, parsed, and crawled by keyword or other metric, while internal data may just need to be reformatted and sanitized for use in analytical data structures.
  • Part of managing the acquired data may involve data quality assessment (306), which may include checking the data for quality and completeness. Incomplete or irrelevant data may be re-categorized or discarded. This may also include error-checking capabilities, exception handling, and recovery. A determination as to the value of the acquired data, and what further analysis is merited, may be performed at the data quality assessment stage.
  • data quality assessment may include checking the data for quality and completeness. Incomplete or irrelevant data may be re-categorized or discarded. This may also include error-checking capabilities, exception handling, and recovery. A determination as to the value of the acquired data, and what further analysis is merited, may be performed at the data quality assessment stage.
  • the data may be aggregated and placed into marts for use (308).
  • the aggregation process will generally involve sorting and combining data according to customer identity.
  • marts may be used, including business partner marts, customer marts, and unstructured marts, to reflect different methods by which data is generated.
  • analyzing, forecasting, and modeling processes may be performed (312).
  • Customer segmentation, statistical analysis, forecasting and extrapolation, predictive modeling, optimization, and data mining may be used to generate a variety of useful results from the data.
  • the data analysis may result in further insights (314).
  • Insights may include the clear delineation of customer segments, root cause analysis, an identification of important trends, threats and opportunity detection, resources and intervention optimization, and context for unstructured data.
  • This data can be presented to and used by the company in a number of ways (316), including alerts, structure queries, feeds for ad hoc reporting, dashboards for static reporting, and insight case management. Any of these can be formatted and digested to be presented to marketing and business managers within the company in any appropriate format.
  • FIG. 4 shows an example of a customer analytic record 400, which may include a variety of data. All of the data associated with one specific customer is included in the record 400, including digital consumption data 402, telecommunications data 404, demographic data 406, and networking data 408. Customer data associated with other products and services provided by the company or other companies may be included, along with any other data acquired internally or from third party sources.
  • each of the data categories 402, 404, 406, and 408 has previously been described with respect to the data model above.
  • the metrics and dimensions discussed above with respect to each of these data categories represents base variables.
  • the CAR 400 may include calculated variables in one or more of these categories, using the example operations listed above or further calculations as appropriate.
  • calculated variables in the category of digital consumption data 402 may include:
  • the CAR variables can be used as an input for predictive analysis and statistical modeling according to business objectives like the segmentation of the customer base or definition of chum propensity of the customers. Once the CAR has been generated, it can be available for use, a variety of analyzing, forecasting, and modeling processes. Customer segmentation, statistical analysis, forecasting and extrapolation, predictive modeling, optimization, and data mining may be used to generate a variety of useful results from the data.
  • FIG. 5 shows an example of a generic computer device 500 and a generic mobile computer device 550, which may be used with the techniques described here.
  • Computing device 500 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers.
  • Computing device 550 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, tablet computers and other similar computing devices.
  • the components shown here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the techniques described and/or claimed in this document.
  • Computing device 500 includes a processor 502, memory 504, a storage device 506, a high-speed interface 508 connecting to memory 504 and high-speed expansion ports 510, and a low speed interface 512 connecting to low speed bus 514 and storage device 506.
  • Each of the components 502, 504, 506, 508, 510, and 512 are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate.
  • the processor 502 can process instructions for execution within the computing device 500, including instructions stored in the memory 504 or on the storage device 506 to display graphical information for a GUI on an external input/output device, such as display 516 coupled to high speed interface 508.
  • multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory.
  • multiple computing devices 500 may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).
  • the memory 504 stores information within the computing device 500.
  • the memory 504 is a volatile memory unit or units.
  • the memory 504 is a non-volatile memory unit or units.
  • the memory 504 may also be another form of computer-readable medium, such as a magnetic or optical disk.
  • the storage device 506 is capable of providing mass storage for the computing device 500.
  • the storage device 506 may be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations.
  • a computer program product can be tangibly embodied in an information carrier.
  • the computer program product may also contain instructions that, when executed, perform one or more methods, such as those described above.
  • the information carrier is a computer- or machine-readable medium, such as the memory 504, the storage device 506, memory on processor 502, or a propagated signal.
  • the high speed controller 508 manages bandwidth-intensive operations for the computing device 500, while the low speed controller 512 manages lower bandwidth-intensive operations.
  • the high-speed controller 508 is coupled to memory 504, display 516 (e.g., through a graphics processor or accelerator), and to high-speed expansion ports 510, which may accept various expansion cards (not shown).
  • low-speed controller 512 is coupled to storage device 506 and low-speed expansion port 514.
  • the low-speed expansion port which may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet) may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.
  • input/output devices such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.
  • the computing device 500 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 520, or multiple times in a group of such servers. It may also be implemented as part of a rack server system 524. In addition, it may be implemented in a personal computer such as a laptop computer 522. Alternatively, components from computing device 500 may be combined with other components in a mobile device (not shown), such as device 550. Each of such devices may contain one or more of computing device 500, 550, and an entire system may be made up of multiple computing devices 500, 550 communicating with each other.
  • Computing device 550 includes a processor 552, memory 564, an input/output device such as a display 554, a communication interface 566, and a transceiver 568, among other components.
  • the device 550 may also be provided with a storage device, such as a microdrive or other device, to provide additional storage.
  • a storage device such as a microdrive or other device, to provide additional storage.
  • Each of the components 550, 552, 564, 554, 566, and 568, are interconnected using various buses, and several of the components may be mounted on a common motherboard or in other manners as appropriate.
  • the processor 552 can execute instructions within the computing device 550, including instructions stored in the memory 564.
  • the processor may be implemented as a chipset of chips that include separate and multiple analog and digital processors.
  • the processor may provide, for example, for coordination of the other components of the device 550, such as control of user interfaces, applications run by device 550, and wireless communication by device 550.
  • Processor 552 may communicate with a user through control interface 558 and display interface 556 coupled to a display 554.
  • the display 554 may be, for example, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display) or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology.
  • the display interface 556 may comprise appropriate circuitry for driving the display 554 to present graphical and other information to a user.
  • the control interface 558 may receive commands from a user and convert them for submission to the processor 552.
  • an external interface 562 may be provide in communication with processor 552, so as to enable near area communication of device 550 with other devices.
  • External interface 562 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.
  • the memory 564 stores information within the computing device 550.
  • the memory 564 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units.
  • Expansion memory 574 may also be provided and connected to device 550 through expansion interface 572, which may include, for example, a SIMM (Single In Line Memory Module) card interface.
  • SIMM Single In Line Memory Module
  • expansion memory 574 may provide extra storage space for device 550, or may also store applications or other information for device 550.
  • expansion memory 574 may include instructions to carry out or supplement the processes described above, and may include secure information also.
  • expansion memory 574 may be provide as a security module for device 550, and may be programmed with instructions that permit secure use of device 550.
  • secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.
  • the memory may include, for example, flash memory and/or NVRAM memory, as discussed below.
  • a computer program product is tangibly embodied in an information carrier.
  • the computer program product contains instructions that, when executed, perform one or more methods, such as those described above.
  • the information carrier is a computer- or machine-readable medium, such as the memory 564, expansion memory 574, memory on processor 552, or a propagated signal that may be received, for example, over transceiver 568 or external interface 562.
  • Device 550 may communicate wirelessly through communication interface 566, which may include digital signal processing circuitry where necessary. Communication interface 566 may provide for communications under various modes or protocols, such as GSM voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others. Such communication may occur, for example, through radio-frequency transceiver 568. In addition, short-range communication may occur, such as using a Bluetooth, WiFi, or other such transceiver (not shown). In addition, GPS (Global Positioning System) receiver module 570 may provide additional navigation- and location-related wireless data to device 550, which may be used as appropriate by applications running on device 550.
  • GPS Global Positioning System
  • Device 550 may also communicate audibly using audio codec 560, which may receive spoken information from a user and convert it to usable digital information. Audio codec 560 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of device 550. Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on device 550.
  • Audio codec 560 may receive spoken information from a user and convert it to usable digital information. Audio codec 560 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of device 550. Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on device 550.
  • the computing device 550 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a cellular telephone 580. It may also be implemented as part of a smartphone 582, personal digital assistant, or other similar mobile device.
  • implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof.
  • ASICs application specific integrated circuits
  • These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
  • the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer.
  • a display device e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor
  • a keyboard and a pointing device e.g., a mouse or a trackball
  • Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
  • the systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components.
  • the components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN”), a wide area network (“WAN”), and the Internet.
  • LAN local area network
  • WAN wide area network
  • the Internet the global information network
  • the computing system can include clients and servers.
  • a client and server are generally remote from each other and typically interact through a communication network.
  • the relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

Landscapes

  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Finance (AREA)
  • Strategic Management (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Engineering & Computer Science (AREA)
  • Game Theory and Decision Science (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
EP12425042.4A 2012-02-27 2012-02-27 Modèle numérique de données de consommation et enregistrement analytique du client Ceased EP2631851A1 (fr)

Priority Applications (5)

Application Number Priority Date Filing Date Title
EP12425042.4A EP2631851A1 (fr) 2012-02-27 2012-02-27 Modèle numérique de données de consommation et enregistrement analytique du client
CA2808096A CA2808096C (fr) 2012-02-27 2013-02-27 Modele de donnees de consommateur numerique et dossier d'analyse des clients
US13/779,115 US9536002B2 (en) 2012-02-27 2013-02-27 Digital consumer data model and customer analytic record
CN201310061925.2A CN103295148B (zh) 2012-02-27 2013-02-27 生成和实现数据模型的方法和装置
CA2955707A CA2955707C (fr) 2012-02-27 2013-02-27 Modele de donnees de consommateur numerique et dossier d'analyse des clients

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP12425042.4A EP2631851A1 (fr) 2012-02-27 2012-02-27 Modèle numérique de données de consommation et enregistrement analytique du client

Publications (1)

Publication Number Publication Date
EP2631851A1 true EP2631851A1 (fr) 2013-08-28

Family

ID=45977326

Family Applications (1)

Application Number Title Priority Date Filing Date
EP12425042.4A Ceased EP2631851A1 (fr) 2012-02-27 2012-02-27 Modèle numérique de données de consommation et enregistrement analytique du client

Country Status (4)

Country Link
US (1) US9536002B2 (fr)
EP (1) EP2631851A1 (fr)
CN (1) CN103295148B (fr)
CA (2) CA2808096C (fr)

Families Citing this family (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9471629B2 (en) * 2013-04-19 2016-10-18 Software AG USA Inc. Method and apparatus for visual dynamic discovery and analytics exploration application on static and/or real-time data using an a priori model-free process
US9595055B2 (en) 2013-11-27 2017-03-14 At&T Intellectual Property I, L.P. Feedback service
EP3111399B1 (fr) * 2014-02-26 2018-10-31 Verto Analytics OY Mesure de profils d'utilisateur de l'internet à multiples écrans, de comportements transactionnels et de structure de population d'utilisateurs par le biais d'un recensement hybride et méthodologie de mesure basée sur l'utilisateur
US9350867B2 (en) * 2014-08-01 2016-05-24 Genesys Telecommunications Laboratories, Inc. System and method for anticipatory dynamic customer segmentation for a contact center
US9848084B2 (en) 2014-08-01 2017-12-19 Genesys Telecommunications Laboratories, Inc. Adaptable business objective routing for a contact center
US9781270B2 (en) * 2014-08-01 2017-10-03 Genesys Telecommunications Laboratories, Inc. System and method for case-based routing for a contact
US10356478B2 (en) * 2015-01-08 2019-07-16 The Directv Group, Inc. Systems and methods for spotted advertising and control of corresponding user interfaces and transactions via user receiving devices and mobile devices
EP3076359A1 (fr) 2015-04-01 2016-10-05 Tata Consultancy Services Ltd. Application d'un modèle de données analytiques de client dans un environnement informatique réparti
US20170053288A1 (en) * 2015-08-18 2017-02-23 LandNExpand, LLC Cloud Based Customer Relationship Mapping
CN105243515B (zh) * 2015-11-09 2022-01-18 浙江中之杰软件技术有限公司 一种企业情况管理***
CN115858730A (zh) * 2016-09-29 2023-03-28 微软技术许可有限责任公司 对话式的数据分析
CN108389060B (zh) * 2018-02-28 2021-10-29 南京芝麻信息科技有限公司 顾客忠诚度信息处理方法及装置
CN114372680A (zh) * 2021-12-23 2022-04-19 电子科技大学(深圳)高等研究院 一种基于工人流失预测的空间众包任务分配方法

Family Cites Families (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001076120A2 (fr) * 2000-04-04 2001-10-11 Stick Networks, Inc. Procede et appareil de planification de la presentation d'un contenu numerique sur un dispositif de communication personnel
US20040032393A1 (en) * 2001-04-04 2004-02-19 Brandenberg Carl Brock Method and apparatus for scheduling presentation of digital content on a personal communication device
WO2004053751A2 (fr) * 2002-12-10 2004-06-24 Accenture Global Services Gmbh Instrument de developpement de modeles de donnees
WO2005099385A2 (fr) * 2004-04-07 2005-10-27 Nielsen Media Research, Inc. Dispositif et procede d'insertion de donnees a utiliser avec des donnees audio/video compressees
US8849862B2 (en) * 2004-05-21 2014-09-30 Rsvpro, Llc Architectural frameworks, functions and interfaces for relationship management (AFFIRM)
EP1788517A1 (fr) * 2005-11-21 2007-05-23 Accenture Global Services GmbH Système et méthode pour analyser la capacité d'un client d'enregistrer des bénéfices
US20070185867A1 (en) * 2006-02-03 2007-08-09 Matteo Maga Statistical modeling methods for determining customer distribution by churn probability within a customer population
CN101094481A (zh) * 2006-06-19 2007-12-26 上海全成通信技术有限公司 数据分析和挖掘***与操作型crm***的互动支撑方法
US20130166580A1 (en) * 2006-12-13 2013-06-27 Quickplay Media Inc. Media Processor
ATE517478T1 (de) * 2007-10-03 2011-08-15 Accenture Global Services Ltd Technologieagnostisches universell anwendbares datenmodell für eine telekommunikationsdienstanbieter-architektur
US8688595B2 (en) * 2008-03-31 2014-04-01 Pursway Ltd. Analyzing transactional data
WO2009158581A2 (fr) * 2008-06-27 2009-12-30 Adpassage, Inc. Système et procédé de reconnaissance de sujet parlé ou de critère dans un contenu numérique et de la publicité contextuelle
US7974983B2 (en) 2008-11-13 2011-07-05 Buzzient, Inc. Website network and advertisement analysis using analytic measurement of online social media content
US20100332373A1 (en) * 2009-02-26 2010-12-30 Jason Crabtree System and method for participation in energy-related markets
US10853826B2 (en) * 2012-02-07 2020-12-01 Yeast, LLC System and method for evaluating and optimizing media content

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
"STATEMENT IN ACCORDANCE WITH THE NOTICE FROM THE EUROPEAN PATENT OFFICE DATED 1 OCTOBER 2007 CONCERNING BUSINESS METHODS - EPC / ERKLAERUNG GEMAESS DER MITTEILUNG DES EUROPAEISCHEN PATENTAMTS VOM 1.OKTOBER 2007 UEBER GESCHAEFTSMETHODEN - EPU / DECLARATION CONFORMEMENT AU COMMUNIQUE DE L'OFFICE EUROP", 20071101, 1 November 2007 (2007-11-01), XP002456252 *

Also Published As

Publication number Publication date
CA2808096C (fr) 2017-03-07
CN103295148B (zh) 2017-03-01
US9536002B2 (en) 2017-01-03
CA2955707A1 (fr) 2013-08-27
CA2808096A1 (fr) 2013-08-27
US20130226657A1 (en) 2013-08-29
CN103295148A (zh) 2013-09-11
CA2955707C (fr) 2020-10-27

Similar Documents

Publication Publication Date Title
US9536002B2 (en) Digital consumer data model and customer analytic record
US10812870B2 (en) Yield optimization of cross-screen advertising placement
US20180005271A1 (en) Information processing method, server, and computer storage medium
Malthouse et al. Managing customer relationships in the social media era: Introducing the social CRM house
US9208202B1 (en) Systems and methods for determining interest in an item or category of items
CN109714610B (zh) 自动式视频营销管理***及方法
US20130085859A1 (en) Targeting Advertisements Based on User Interactions
US20170061500A1 (en) Systems and methods for data service platform
Rosemann et al. Leveraging social network data for analytical CRM strategies-the introduction of social BI
US20180253769A1 (en) Method and apparatus for selecting and recommending presentation objects on electronic distribution platforms
KR102000076B1 (ko) 온라인 쇼핑몰 통합 관리 시스템에서의 온라인 판매채널을 추천하는 방법 및 그 서버
JP5253519B2 (ja) スマートテキストを生成するための方法、装置、及び記憶媒体
US20150032503A1 (en) System and Method for Customer Evaluation and Retention
Reimer et al. How online consumer segments differ in long-term marketing effectiveness
JP2012079317A (ja) ユーザコミュニティにターゲット情報を効果的に提供するためのシステム及び方法
Scholz et al. Awareness, interest, and purchase: The effects of user-and marketer-generated content on purchase decision processes
Liao et al. Big data analytics of social network marketing and personalized recommendations
WO2023217194A1 (fr) Procédé de génération de couverture vidéo dynamique
US20220377424A1 (en) Dynamic digital content delivery using artificial intelligence (ai) techniques
Chen High-concurrency big data precision marketing and advertising recommendation under 5g wireless communication network environment
US20150339723A1 (en) User-based analysis of advertisement pools
Mac Cathmhaoil et al. Digital business model internationalisation: illustrative cases of born global digital companies
DuttaRoy SAP Business Analytics: A Best Practices Guide for Implementing Business Analytics Using SAP
Zhu et al. Unintended Consequences of Platform Monetization on Digital Cultural Markets: Evidence from a Natural Experiment on Goodreads
Benzoni et al. The Internet: A new economic paradigm for the advertising market

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

AX Request for extension of the european patent

Extension state: BA ME

17P Request for examination filed

Effective date: 20140206

RBV Designated contracting states (corrected)

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

17Q First examination report despatched

Effective date: 20140305

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION HAS BEEN REFUSED

18R Application refused

Effective date: 20141016